Cargando…
Transfer Learning-Based Autosegmentation of Primary Tumor Volumes of Glioblastomas Using Preoperative MRI for Radiotherapy Treatment
OBJECTIVES: Glioblastoma is the most common primary malignant brain tumor in adults and can be treated with radiation therapy. However, tumor target contouring for head radiation therapy is labor-intensive and highly dependent on the experience of the radiation oncologist. Recently, autosegmentation...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9047780/ https://www.ncbi.nlm.nih.gov/pubmed/35494067 http://dx.doi.org/10.3389/fonc.2022.856346 |
_version_ | 1784695795835994112 |
---|---|
author | Tian, Suqing Wang, Cuiying Zhang, Ruiping Dai, Zhuojie Jia, Lecheng Zhang, Wei Wang, Junjie Liu, Yinglong |
author_facet | Tian, Suqing Wang, Cuiying Zhang, Ruiping Dai, Zhuojie Jia, Lecheng Zhang, Wei Wang, Junjie Liu, Yinglong |
author_sort | Tian, Suqing |
collection | PubMed |
description | OBJECTIVES: Glioblastoma is the most common primary malignant brain tumor in adults and can be treated with radiation therapy. However, tumor target contouring for head radiation therapy is labor-intensive and highly dependent on the experience of the radiation oncologist. Recently, autosegmentation of the tumor target has been playing an increasingly important role in the development of radiotherapy plans. Therefore, we established a deep learning model and improved its performance in autosegmenting and contouring the primary gross tumor volume (GTV) of glioblastomas through transfer learning. METHODS: The preoperative MRI data of 20 patients with glioblastomas were collected from our department (ST) and split into a training set and testing set. We fine-tuned a deep learning model for autosegmentation of the hippocampus on separate MRI scans (RZ) through transfer learning and trained this deep learning model directly using the training set. Finally, we evaluated the performance of both trained models in autosegmenting glioblastomas using the testing set. RESULTS: The fine-tuned model converged within 20 epochs, compared to over 50 epochs for the model trained directly by the same training set, and demonstrated better autosegmentation performance [Dice similarity coefficient (DSC) 0.9404 ± 0.0117, 95% Hausdorff distance (95HD) 1.8107 mm ±0.3964mm, average surface distance (ASD) 0.6003 mm ±0.1287mm] than the model trained directly (DSC 0.9158±0.0178, 95HD 2.5761 mm ± 0.5365mm, ASD 0.7579 mm ± 0.1468mm) with the same test set. The DSC, 95HD, and ASD values of the two models were significantly different (P<0.05). CONCLUSION: A model developed with semisupervised transfer learning and trained on independent data achieved good performance in autosegmenting glioblastoma. The autosegmented volume of glioblastomas is sufficiently accurate for radiotherapy treatment, which could have a positive impact on tumor control and patient survival. |
format | Online Article Text |
id | pubmed-9047780 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90477802022-04-29 Transfer Learning-Based Autosegmentation of Primary Tumor Volumes of Glioblastomas Using Preoperative MRI for Radiotherapy Treatment Tian, Suqing Wang, Cuiying Zhang, Ruiping Dai, Zhuojie Jia, Lecheng Zhang, Wei Wang, Junjie Liu, Yinglong Front Oncol Oncology OBJECTIVES: Glioblastoma is the most common primary malignant brain tumor in adults and can be treated with radiation therapy. However, tumor target contouring for head radiation therapy is labor-intensive and highly dependent on the experience of the radiation oncologist. Recently, autosegmentation of the tumor target has been playing an increasingly important role in the development of radiotherapy plans. Therefore, we established a deep learning model and improved its performance in autosegmenting and contouring the primary gross tumor volume (GTV) of glioblastomas through transfer learning. METHODS: The preoperative MRI data of 20 patients with glioblastomas were collected from our department (ST) and split into a training set and testing set. We fine-tuned a deep learning model for autosegmentation of the hippocampus on separate MRI scans (RZ) through transfer learning and trained this deep learning model directly using the training set. Finally, we evaluated the performance of both trained models in autosegmenting glioblastomas using the testing set. RESULTS: The fine-tuned model converged within 20 epochs, compared to over 50 epochs for the model trained directly by the same training set, and demonstrated better autosegmentation performance [Dice similarity coefficient (DSC) 0.9404 ± 0.0117, 95% Hausdorff distance (95HD) 1.8107 mm ±0.3964mm, average surface distance (ASD) 0.6003 mm ±0.1287mm] than the model trained directly (DSC 0.9158±0.0178, 95HD 2.5761 mm ± 0.5365mm, ASD 0.7579 mm ± 0.1468mm) with the same test set. The DSC, 95HD, and ASD values of the two models were significantly different (P<0.05). CONCLUSION: A model developed with semisupervised transfer learning and trained on independent data achieved good performance in autosegmenting glioblastoma. The autosegmented volume of glioblastomas is sufficiently accurate for radiotherapy treatment, which could have a positive impact on tumor control and patient survival. Frontiers Media S.A. 2022-04-14 /pmc/articles/PMC9047780/ /pubmed/35494067 http://dx.doi.org/10.3389/fonc.2022.856346 Text en Copyright © 2022 Tian, Wang, Zhang, Dai, Jia, Zhang, Wang and Liu https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Tian, Suqing Wang, Cuiying Zhang, Ruiping Dai, Zhuojie Jia, Lecheng Zhang, Wei Wang, Junjie Liu, Yinglong Transfer Learning-Based Autosegmentation of Primary Tumor Volumes of Glioblastomas Using Preoperative MRI for Radiotherapy Treatment |
title | Transfer Learning-Based Autosegmentation of Primary Tumor Volumes of Glioblastomas Using Preoperative MRI for Radiotherapy Treatment |
title_full | Transfer Learning-Based Autosegmentation of Primary Tumor Volumes of Glioblastomas Using Preoperative MRI for Radiotherapy Treatment |
title_fullStr | Transfer Learning-Based Autosegmentation of Primary Tumor Volumes of Glioblastomas Using Preoperative MRI for Radiotherapy Treatment |
title_full_unstemmed | Transfer Learning-Based Autosegmentation of Primary Tumor Volumes of Glioblastomas Using Preoperative MRI for Radiotherapy Treatment |
title_short | Transfer Learning-Based Autosegmentation of Primary Tumor Volumes of Glioblastomas Using Preoperative MRI for Radiotherapy Treatment |
title_sort | transfer learning-based autosegmentation of primary tumor volumes of glioblastomas using preoperative mri for radiotherapy treatment |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9047780/ https://www.ncbi.nlm.nih.gov/pubmed/35494067 http://dx.doi.org/10.3389/fonc.2022.856346 |
work_keys_str_mv | AT tiansuqing transferlearningbasedautosegmentationofprimarytumorvolumesofglioblastomasusingpreoperativemriforradiotherapytreatment AT wangcuiying transferlearningbasedautosegmentationofprimarytumorvolumesofglioblastomasusingpreoperativemriforradiotherapytreatment AT zhangruiping transferlearningbasedautosegmentationofprimarytumorvolumesofglioblastomasusingpreoperativemriforradiotherapytreatment AT daizhuojie transferlearningbasedautosegmentationofprimarytumorvolumesofglioblastomasusingpreoperativemriforradiotherapytreatment AT jialecheng transferlearningbasedautosegmentationofprimarytumorvolumesofglioblastomasusingpreoperativemriforradiotherapytreatment AT zhangwei transferlearningbasedautosegmentationofprimarytumorvolumesofglioblastomasusingpreoperativemriforradiotherapytreatment AT wangjunjie transferlearningbasedautosegmentationofprimarytumorvolumesofglioblastomasusingpreoperativemriforradiotherapytreatment AT liuyinglong transferlearningbasedautosegmentationofprimarytumorvolumesofglioblastomasusingpreoperativemriforradiotherapytreatment |